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- import math
- def online_network_benchmarking(network, path_list, bounces):
- # Initialization
- candidate_set = path_list
- s = 0 # Phase
- C = 0.01 # Constant
- delta = 0.1 # Error
- cost = 0
- estimated_fidelities = {}
- # error_probability = {} # Map bounces number to the probability that the arm with largest mean is not the best arm
- # epoch_len = 20
- # epoch = 0
- # correct_times = 0
- while len(candidate_set) > 1:
- s += 1
- Ns = math.ceil(C * 2**(2 * s) * math.log2(2**s * len(candidate_set) / delta))
- if Ns < 4:
- Ns = 4
- # print(f"Ns: {Ns}")
- sample_times = {}
- for i in bounces:
- sample_times[i] = Ns
- p_s = {}
- for path in candidate_set:
- p, bounces_num = network.benchmark_path(path, bounces, sample_times)
- # print(f"Estimated Fidelity of path {path}: {p}")
- # Convert the estimated depolarizing parameter `p` into fidelity
- estimated_fidelities[path] = p + (1 - p) / 2
- p_s[path] = p
- cost += bounces_num
- p_max = max(p_s.values())
- # current_best_path = max(p_s, key=p_s.get)
- # if current_best_path == network.best_path:
- # correct_times += 1
- new_candidate_set = []
- for path in candidate_set:
- # print(f"p_s[path] + 2**(-s): {p_s[path] + 2**(-s)}")
- # print(f"p_max - 2**(-s): {p_max - 2**(-s)}")
- if p_s[path] + 2**(-s) > p_max - 2**(-s):
- new_candidate_set.append(path)
- candidate_set = new_candidate_set
- assert len(candidate_set) == 1
- best_path = candidate_set[0]
- correctness = best_path == network.best_path
- best_path_fidelity = estimated_fidelities[best_path]
- # print(f"Best path: {best_path}, estimated parameter p: {p_s[best_path]}, cost: {cost}")
- return correctness, cost, best_path_fidelity
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